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An analysis description language is a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing framework. It is designed for use by anyone with an interest in, and knowledge of, LHC physics, i.e., experimentalists, phenomenologists and other enthusiasts. Adopting analysis description languages would bring numerous benefits for the LHC experimental and phenomenological communities ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Here, we introduce the analysis description language concept and summarize the current efforts ongoing to develop such languages and tools to use them in LHC analyses.
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We present CutLang, an analysis description language and runtime interpreter for high energy collider physics data analyses. An analysis description language is a declerative domain specific language that can express all elements of a data analysis in an easy and unambiguous way. A full-fledged human readable analysis description language, incorporating logical and mathematical expressions, would eliminate many programming difficulties and errors, consequently allowing the scientist to focus on the goal, but not on the tool. In this paper, we discuss the guiding principles and scope of the CutLang language, implementation of the CutLang runtime interpreter and the CutLang framework, and demonstrate an example of top pair reconstruction.
The fifth edition of the Computing Applications in Particle Physics school was held on 3-7 February 2020, at Istanbul University, Turkey. This particular edition focused on the processing of simulated data from the Large Hadron Collider collisions using an Analysis Description Language and its runtime interpreter called CutLang. 24 undergraduate and 6 graduate students were initiated to collider data analysis during the school. After 3 days of lectures and exercises, the students were grouped into teams of 3 or 4 and each team was assigned an analysis publication from ATLAS or CMS experiments. After 1.5 days of independent study, each team was able to reproduce the assigned analysis using CutLang.
We present a method for improving the phenomenological description of Mueller-Navelet jets at LHC, which is based on matching the BFKL resummation with fixed order calculations. We point out the need of a consistent identification of jets between experimental measurements and theoretical descriptions. We hope as well to motivate an extensive analysis of MN jets at LHC in run 2.
Loop-induced $ZZ$ production can be enhanced by the large gluon flux at the LHC, and thus should be taken into account in relevant experimental analyses. We present for the first time the results of a fully exclusive simulation based on the matrix elements for loop-induced $ZZ + 0,1,2$-parton processes at leading order, matched to parton showers. The new description is studied and validated by comparing it with well-established simulation with jets from parton showers. We find that the matched simulation provides a state-of-the-art description of the final state jets. We also briefly discuss the physics impact on vector boson scattering (VBS) measurements at the LHC, where event yields are found to be smaller by about 40% in a VBS $ZZjj$ baseline search region, compared to previous simulations. We hence advocate relevant analyses to employ a more accurate jet description for the modeling of the loop-induced process.
Traditional linguistic theories have largely regard language as a formal system composed of rigid rules. However, their failures in processing real language, the recent successes in statistical natural language processing, and the findings of many psychological experiments have suggested that language may be more a probabilistic system than a formal system, and thus cannot be faithfully modeled with the either/or rules of formal linguistic theory. The present study, based on authentic language data, confirmed that those important linguistic issues, such as linguistic universal, diachronic drift, and language variations can be translated into probability and frequency patterns in parole. These findings suggest that human language may well be probabilistic systems by nature, and that statistical may well make inherent properties of human languages.
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